Abnormal data detecting method suitable for wireless sensor network

An abnormal data detection and wireless sensor technology, applied in the field of network anomaly detection, can solve problems such as high false alarm rate, low detection rate, unreasonable allocation of computing resources, etc., to reduce false alarm rate, improve detection rate, and highlight the essence The effect of sexuality

Active Publication Date: 2018-10-12
NANJING UNIV OF POSTS & TELECOMM
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Problems solved by technology

[0005] In view of this, the purpose of the present invention is to propose a method for abnormal data detection suitable for wireless sensor networks, to solve the problems of unreasonable allocation of computing resources, high false alarm rate, and low detection rate in the detection of abnormal data in such networks

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  • Abnormal data detecting method suitable for wireless sensor network
  • Abnormal data detecting method suitable for wireless sensor network

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[0024] The technical solutions of the present invention will be further described in detail below in conjunction with the accompanying drawings, and the examples given are only used to explain the present invention, and are not intended to limit the scope of the present invention.

[0025] 1. Clustering of network nodes

[0026] The classic clustering algorithms in wireless sensor networks mainly include: Santi's improved GAF ​​(Geographical Adaptive Fidelity) clustering algorithm, Deb's TopDisc (Topology Discovery) topology discovery algorithm, Heinzelman's LEACH (LOW Energy Adaptive Clustering Hierarchy) algorithm and Younis' HEED algorithm etc. Among them, the most classic one is the TopDisc algorithm of the minimum dominating set theory, which uses a greedy algorithm to select the backbone nodes in the network, and is specifically divided into two types: three-color method and four-color method. Some scholars improved the scheme and proposed the Power-Balanced TopDisc alg...

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Abstract

The invention discloses an abnormal data detecting method suitable for a wireless sensor network, which comprises the steps of clustering of network nodes: according to a clustering algorithm, the nodes related to data acquisition are sorted into one group, and one cluster head node of the group of nodes is selected; double detection: each node obtains a self-differentiation threshold through a training data set combined with the PCA algorithm, a global differentiation threshold is calculated by the cluster head node of the corresponding cluster, and any node carries out detection based on theself-differentiation threshold and the global differentiation threshold after detecting new data; detection model update: the composition of the training data set is changed along with the new detection data. The abnormal data detecting method suitable for the wireless sensor network in the invention fully utilizes the time and space correlation characteristics between the sensing data of the wireless sensor network nodes, proposes a dual detection mechanism combining local detection and global detection based on PCA, and proposes an update scheme of the abnormality detection model, which greatly improves the reliability of the abnormality detection model. The scheme is more suitable for detecting abnormal data of sensor networks in actual scenes.

Description

technical field [0001] The invention relates to a method for detecting network anomalies, in particular to an abnormal data detection method for densely deployed wireless sensor networks. Background technique [0002] With the rapid development of network technology, the existing abnormal data detection schemes for wireless sensor networks are mainly divided into two types, namely centralized detection schemes and distributed detection schemes. Specifically analyze its characteristics, advantages and disadvantages: the core idea of ​​the centralized detection scheme is to uniformly send the detection data of each node to the sink node, and the sink node receives the data sent by each node, and extracts the abnormality according to the abnormal data detection model of the sink node data, and locate the corresponding abnormal node. The advantage of this approach is that each sensor node does not require additional computational overhead and only needs to pass data to the sink...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04W4/38H04W4/70H04L29/06
CPCH04L63/1425H04W4/38H04W4/70
Inventor 杨立君郑文添吴蒙
Owner NANJING UNIV OF POSTS & TELECOMM
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